Federated Learning

Federated Learning is a machine learning approach that allows multiple devices or entities to collaboratively train a shared model without exchanging their data with each other. Instead of sending data to a central server for training, the model is trained locally on each device, and only the model updates are sent to the central server, where they are aggregated to improve the shared model.

This approach allows for privacy-preserving machine learning, as each device keeps its data locally and only shares the information needed to improve the model.

Papers

Recently Added Most Hyped Most Active Needs Verification Most Verified

Showing 1351–1375 of 6771 papers

Title	Date	Tasks	Status	Score
Federated Intelligence for Active Queue Management in Inter-Domain Congestion	Jan 8, 2021	Federated LearningManagement	CodeCode Available	5
Federated Frank-Wolfe Algorithm	Aug 19, 2024	Federated LearningPrivacy Preserving	CodeCode Available	5
Federated Few-shot Learning for Cough Classification with Edge Devices	Sep 3, 2023	Cough ClassificationFederated Learning	CodeCode Available	5
Real World Federated Learning with a Knowledge Distilled Transformer for Cardiac CT Imaging	Jul 10, 2024	Federated LearningPartially Labeled Datasets	CodeCode Available	5
Federated Learning for Data Streams	Jan 4, 2023	Federated Learning	CodeCode Available	5
Federated Learning of Large Models at the Edge via Principal Sub-Model Training	Aug 28, 2022	Federated Learning	CodeCode Available	5
Federated Learning with Reservoir State Analysis for Time Series Anomaly Detection	Feb 8, 2025	Anomaly DetectionComputational Efficiency	CodeCode Available	5
Principled Federated Domain Adaptation: Gradient Projection and Auto-Weighting	Feb 10, 2023	Domain AdaptationFederated Learning	CodeCode Available	5
Comparative Evaluation of Clustered Federated Learning Methods	Oct 18, 2024	Federated Learningimage-classification	CodeCode Available	5
A Survey of Incremental Transfer Learning: Combining Peer-to-Peer Federated Learning and Domain Incremental Learning for Multicenter Collaboration	Sep 29, 2023	Continual LearningFederated Learning	CodeCode Available	5
Federated Document Visual Question Answering: A Pilot Study	May 10, 2024	Federated LearningQuestion Answering	CodeCode Available	5
CDMA: A Practical Cross-Device Federated Learning Algorithm for General Minimax Problems	May 29, 2021	Federated LearningGenerative Adversarial Network	CodeCode Available	5
Federated Deep AUC Maximization for Heterogeneous Data with a Constant Communication Complexity	Feb 9, 2021	Federated Learning	CodeCode Available	5
Federated Face Forgery Detection Learning with Personalized Representation	Jun 17, 2024	Federated LearningPersonalized Federated Learning	CodeCode Available	5
Dancing in the Dark: Private Multi-Party Machine Learning in an Untrusted Setting	Nov 23, 2018	BIG-bench Machine LearningFederated Learning	CodeCode Available	5
Communication Resources Constrained Hierarchical Federated Learning for End-to-End Autonomous Driving	Jun 28, 2023	Autonomous DrivingFederated Learning	CodeCode Available	5
Federated clustering with GAN-based data synthesis	Oct 29, 2022	ClusteringFederated Learning	CodeCode Available	5
Federated Collaborative Filtering for Privacy-Preserving Personalized Recommendation System	Jan 29, 2019	BIG-bench Machine LearningCollaborative Filtering	CodeCode Available	5
Federated Continual Learning for Text Classification via Selective Inter-client Transfer	Oct 12, 2022	Continual LearningFederated Learning	CodeCode Available	5
Communication-Efficient Zeroth-Order Distributed Online Optimization: Algorithm, Theory, and Applications	Jun 9, 2023	Federated Learning	CodeCode Available	5
Attacks on fairness in Federated Learning	Nov 21, 2023	AttributeBackdoor Attack	CodeCode Available	5
Federated Classification in Hyperbolic Spaces via Secure Aggregation of Convex Hulls	Aug 14, 2023	Federated Learninggraph partitioning	CodeCode Available	5
Attention on Personalized Clinical Decision Support System: Federated Learning Approach	Jan 22, 2024	Disease PredictionFederated Learning	CodeCode Available	5
Communication-efficient Vertical Federated Learning via Compressed Error Feedback	Jun 20, 2024	Federated LearningVertical Federated Learning	CodeCode Available	5
Federated Black-Box Adaptation for Semantic Segmentation	Oct 31, 2024	Federated LearningSemantic Segmentation	CodeCode Available	5

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All datasets Cityscapes heterogeneous Landmarks-User-160k CIFAR-100 (alpha=0, 10 clients per round)CIFAR-100 (alpha=0, 20 clients per round)CIFAR-100 (alpha=0.5, 10 clients per round)CIFAR-100 (alpha=0.5, 20 clients per round)CIFAR-100 (alpha=0.5, 5 clients per round)CIFAR-100 (alpha=0, 5 clients per round)CIFAR-100 (alpha=1000, 10 clients per round)CIFAR-100 (alpha=1000, 20 clients per round)CIFAR-100 (alpha=1000, 5 clients per round)CIFAR100 (alpha=0.3, 10 clients per round)

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	SiloBN + ASAM	mIoU	49.75	—	Unverified
2	SiloBN + SAM	mIoU	49.1	—	Unverified
3	SiloBN	mIoU	45.96	—	Unverified
4	FedSAM + SWA	mIoU	43.42	—	Unverified
5	FedASAM + SWA	mIoU	43.02	—	Unverified
6	FedAvg + SWA	mIoU	42.48	—	Unverified
7	FedASAM	mIoU	42.27	—	Unverified
8	FedSAM	mIoU	41.22	—	Unverified
9	FedAvg	mIoU	38.65	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	FedASAM + SWA	Acc@1-1262Clients	68.32	—	Unverified
2	FedSAM + SWA	Acc@1-1262Clients	68.12	—	Unverified
3	FedAvg + SWA	Acc@1-1262Clients	67.52	—	Unverified
4	FedASAM	Acc@1-1262Clients	64.23	—	Unverified
5	FedSAM	Acc@1-1262Clients	63.72	—	Unverified
6	FedAvg	Acc@1-1262Clients	61.91	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	FedASAM + SWA	ACC@1-100Clients	42.64	—	Unverified
2	FedASAM	ACC@1-100Clients	39.76	—	Unverified
3	FedSAM + SWA	ACC@1-100Clients	39.51	—	Unverified
4	FedSAM	ACC@1-100Clients	36.93	—	Unverified
5	FedAvg	ACC@1-100Clients	36.74	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	FedASAM + SWA	ACC@1-100Clients	41.62	—	Unverified
2	FedASAM	ACC@1-100Clients	40.81	—	Unverified
3	FedSAM + SWA	ACC@1-100Clients	39.24	—	Unverified
4	FedAvg	ACC@1-100Clients	38.59	—	Unverified
5	FedSAM	ACC@1-100Clients	38.56	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	FedASAM + SWA	ACC@1-100Clients	48.72	—	Unverified
2	FedSAM + SWA	ACC@1-100Clients	46.76	—	Unverified
3	FedASAM	ACC@1-100Clients	46.58	—	Unverified
4	FedSAM	ACC@1-100Clients	44.84	—	Unverified
5	FedAvg	ACC@1-100Clients	41.27	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	FedASAM + SWA	ACC@1-100Clients	48.27	—	Unverified
2	FedASAM	ACC@1-100Clients	47.78	—	Unverified
3	FedSAM + SWA	ACC@1-100Clients	46.47	—	Unverified
4	FedSAM	ACC@1-100Clients	46.05	—	Unverified
5	FedAvg	ACC@1-100Clients	42.17	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	FedASAM + SWA	ACC@1-100Clients	49.17	—	Unverified
2	FedSAM + SWA	ACC@1-100Clients	47.96	—	Unverified
3	FedASAM	ACC@1-100Clients	45.61	—	Unverified
4	FedSAM	ACC@1-100Clients	44.73	—	Unverified
5	FedAvg	ACC@1-100Clients	40.43	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	FedASAM + SWA	ACC@1-100Clients	42.01	—	Unverified
2	FedSAM + SWA	ACC@1-100Clients	39.3	—	Unverified
3	FedASAM	ACC@1-100Clients	36.04	—	Unverified
4	FedSAM	ACC@1-100Clients	31.04	—	Unverified
5	FedAvg	ACC@1-100Clients	30.25	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	FedASAM	ACC@1-100Clients	54.97	—	Unverified
2	FedASAM + SWA	ACC@1-100Clients	54.79	—	Unverified
3	FedSAM + SWA	ACC@1-100Clients	53.67	—	Unverified
4	FedSAM	ACC@1-100Clients	53.39	—	Unverified
5	FedAvg	ACC@1-100Clients	50.25	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	FedASAM	ACC@1-100Clients	54.5	—	Unverified
2	FedSAM + SWA	ACC@1-100Clients	54.36	—	Unverified
3	FedASAM + SWA	ACC@1-100Clients	54.1	—	Unverified
4	FedSAM	ACC@1-100Clients	53.97	—	Unverified
5	FedAvg	ACC@1-100Clients	50.66	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	FedASAM	ACC@1-100Clients	54.81	—	Unverified
2	FedSAM	ACC@1-100Clients	54.01	—	Unverified
3	FedSAM + SWA	ACC@1-100Clients	53.9	—	Unverified
4	FedASAM + SWA	ACC@1-100Clients	53.86	—	Unverified
5	FedAvg	ACC@1-100Clients	49.92	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	AdaBest	Average Top-1 Accuracy	56.2	—	Unverified